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Timeouts in distributed systems indicate suspicion, not confirmed failure.

Fallbacks allow systems to handle failures safely and maintain user experience.

Partial failure means some services fail while others keep running, impacting system reliability.

Dependency failures can disrupt applications, but resilience patterns can help manage them.

A Circuit Breaker helps prevent system failures by stopping calls to unhealthy services.

Alerting helps notify the right people about production issues quickly.

Classifying failures and using controlled retries in Kafka prevents outages.

Kafka uses retries and Dead Letter Queues to manage message processing failures.

Cascading failures occur when one service's failure impacts others, causing widespread issues.

The Circuit Breaker pattern controls service calls to prevent failures from spreading.

Cache penetration occurs when requests for non-existent data overload the database.

The retry pattern helps systems recover from temporary failures but can cause overload if misused.

The Bulkhead Pattern isolates resources to protect critical workloads from failures.

Kafka uses replication and in-sync replicas to ensure data is always available.

Database replication helps keep data available and allows systems to handle more read requests.

Distributed locks ensure only one server performs a task, avoiding duplication.

Availability is about access; reliability is about correct performance.

Kafka can help ensure messages are processed once, but requires careful design.

Choosing the right distributed lock depends on your system's needs and existing tools.